Oxygen&Voltage_gradients

Introduction

This rmd. file imports oxygen and voltage data from Google sheet Data is used to plot a oxygen and voltage gradient map of the winogradsky column Exponential and linear models were used to analyze data

#Loading Packages

library(tidyverse) 
library(googledrive) 
library(googlesheets4)
#library(ggplot2)
library(knitr)
library(minpack.lm)

Warning: package ‘minpack.lm’ was built under R version 4.4.2

library(broom)
library(purrr)
library(lme4)

Warning: package ‘lme4’ was built under R version 4.4.2Loading required package: Matrix

Attaching package: ‘Matrix’

The following objects are masked from ‘package:tidyr’:

    expand, pack, unpack

#Importing from google sheet

googlesheets4::gs4_deauth()

CleanData_Oxygen <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1hNakCyqPsTNRFpf1lGjoBQ-papyZ6xHKT6mcvh3LMb8/edit?pli=1&gid=842906991#gid=842906991", "CleanData_Oxygen") |>
  mutate(Sample = as.character(Sample))

✔ Reading from Microcosms.
✔ Range ''CleanData_Oxygen''.

kable(CleanData_Oxygen) 

Sample	Depth_cm	DO_µg_L	temp_C
1004	0.0	276.0	21.2
1004	6.2	276.0	21.2
1004	8.4	275.0	21.2
1004	13.1	6.3	21.2
1004	22.0	3.4	21.2
1003	0.0	101.0	20.0
1003	5.8	62.0	20.0
1003	8.4	52.0	20.0
1003	13.1	29.0	20.0
1003	21.3	21.0	20.0
1002	0.0	80.0	19.8
1002	6.6	45.0	19.8
1002	9.3	26.0	19.8
1002	15.1	21.0	19.8
1002	21.9	19.0	19.8
1001_C	0.0	190.0	19.0
1001_C	6.9	106.0	19.0
1001_C	10.8	99.0	19.0
1001_C	16.6	57.0	19.0
1001_C	22.7	25.0	19.0
1001_W	0.0	103.0	19.0
1001_W	6.7	34.0	19.0
1001_W	10.2	21.0	19.0
1001_W	15.2	11.0	19.0
1001_W	22.8	8.0	19.0

googlesheets4::gs4_deauth()

CleanData_Voltage <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1hNakCyqPsTNRFpf1lGjoBQ-papyZ6xHKT6mcvh3LMb8/edit?pli=1&gid=842906991#gid=842906991", "CleanData_Voltage") |>
  mutate(Sample = as.character(Sample))

✔ Reading from Microcosms.
✔ Range ''CleanData_Voltage''.

kable(CleanData_Voltage)

Sample	Depth_cm	Voltage_Water_10s	Voltage_Water_Max	Voltage_Tap_10s	Voltage_Tap_Max
1004	0.0	NA	NA	121.0	121.0
1004	6.2	160.2	190	-168.0	-168.0
1004	8.4	74.4	79	84.0	122.0
1004	13.1	-55.0	58	43.0	51.0
1004	22.0	-200.0	204	84.0	102.0
1003	0.0	NA	NA	74.0	300.0
1003	5.8	27.0	27	-13.0	26.0
1003	8.4	59.0	83	57.0	57.0
1003	13.1	-20.0	101	-94.0	-136.0
1003	21.3	-25.0	-30	161.0	177.0
1002	0.0	NA	NA	51.0	199.0
1002	6.6	-39.0	-40	13.0	30.0
1002	9.3	-4.3	30	196.0	199.0
1002	15.1	24.0	-4	16.0	25.0
1002	21.9	27.0	28	194.0	199.0
1001_C	0.0	NA	NA	187.0	187.0
1001_C	6.9	-10.0	-22	76.0	300.0
1001_C	10.8	25.0	60	-2.8	-2.8
1001_C	16.6	-6.0	-23	-10.5	-3.0
1001_C	22.7	6.0	48	168.0	174.0
1001_W	0.0	NA	NA	187.0	187.0
1001_W	6.7	16.0	-9	13.0	170.0
1001_W	10.2	31.0	33	-48.0	-48.0
1001_W	15.2	34.0	40	24.0	24.0
1001_W	22.8	40.0	-17	72.0	72.0

Combine O2 & Voltage data by sample & depth

Gradients <- left_join(x = CleanData_Oxygen, y = CleanData_Voltage, by = c("Sample", "Depth_cm"))

#Oxygen Gradient Plot

ggplot(data = CleanData_Oxygen) +
geom_point(aes(y = DO_µg_L, x = Depth_cm)) + 
  #scale_y_reverse() +
  #labs( title= "Oxygen Gradients", caption= "Figure X. Dissolved Oxygen (DO) (µg/L) measured in four Winogradsky colums at 5 depths (cm)") 
  #+ theme(plot.caption= element_text(size = 11, hjust=0)) +
  #geom_path(aes(y = Depth_cm, x = DO_µg_L)) +
  facet_grid(cols = vars(Sample)) +
  theme_bw()

#+ scale_y_continuous(limits = c(0, 300))

#define exponential decay function for data fitting.
exp_decay <- function(x, i, mu){y = i * exp(mu * x)}


O2_nest <- CleanData_Oxygen |>  #alternate forward pipe is %>% loaded with tidyverse
  nest(.by = "Sample") |>
  mutate(DecayFit = purrr::map(data, ~nlsLM(DO_µg_L ~ exp_decay(x = Depth_cm, i, mu),
                                            data = .x)),
         DecayTidy = purrr::map(DecayFit, tidy),
         DecayParam = purrr::map(DecayFit, glance),
         DecayPredict = purrr::map(DecayFit, augment)
         )

Warning: There were 5 warnings in `mutate()`.
The first warning was:
ℹ In argument: `DecayFit = purrr::map(...)`.
Caused by warning in `nlsLM()`:
! No starting values specified for some parameters.
Initializing ‘i’, ‘mu’ to '1.'.
Consider specifying 'start' or using a selfStart model
ℹ Run ]8;;ide:run:dplyr::last_dplyr_warnings()dplyr::last_dplyr_warnings()]8;; to see the 4 remaining warnings.

O2_nest |>
  unnest(cols = c(DecayPredict)) |>
  ggplot() +
  geom_point(aes(x = Depth_cm, y = DO_µg_L)) +
  geom_line(aes(x = Depth_cm, y = .fitted)) +
  geom_point(aes(x = Depth_cm, y = .resid), colour = "red") +
  facet_grid(cols = vars(unlist(Sample))) +
  theme_bw()

O2_nest |>
unnest(cols = c(DecayTidy)) |>
 select(-c(data, DecayFit, DecayParam, DecayPredict)) |>
  select(-c(statistic)) |>
  pivot_wider(id_cols = Sample, names_from = term, values_from = c(estimate, std.error, p.value)) |>
  kable()

Sample	estimate_i	estimate_mu	std.error_i	std.error_mu	p.value_i	p.value_mu
1004	326.62312	-0.0779518	101.553082	0.0494649	0.0487222	0.2131346
1003	100.85694	-0.0833277	3.557277	0.0059295	0.0000963	0.0007804
1002	78.61257	-0.0897172	6.441339	0.0139884	0.0011845	0.0076805
1001_C	190.11905	-0.0743515	10.391455	0.0078457	0.0003563	0.0024909
1001_W	102.49196	-0.1553412	3.251191	0.0097456	0.0000701	0.0005369

ggplot(data = Gradients) +
  geom_point(aes(x = Depth_cm, y = Voltage_Water_10s)) + 
  facet_grid(cols = vars(Sample)) + 
  theme_bw()

Gradients %>%
  ggplot() +
 geom_point(aes(x = Depth_cm, y = Voltage_Water_10s)) + 
  geom_smooth(aes( x = Depth_cm, y = Voltage_Water_10s), method = "lm", se=FALSE) +
  #facet_wrap(vars((Sample))) +
facet_grid(cols = vars(Sample)) + 
  theme_bw()

 # Fit the linear model grouped by Sample
linear_fits <- Gradients %>%
  group_by(Sample) %>%
  group_map(~ {
    model <- lm(Voltage_Water_10s ~ Depth_cm, data = .x) 
   # Get the tidy model summary
     tidy_model <- tidy(model)  
   # Add the 'Sample' variable back into the final table display
      tidy_model$Sample <- unique(.x$Sample)  
      # Return the tidy model 
    return(tidy_model)  
  }, .keep = TRUE) %>%
  bind_rows() %>%
  # Filter for 'Depth_cm' coefficient (we are examining Voltage in terms of Depth_cm)
  filter(term == "Depth_cm")  

# Selecting to include Sample, estimate of slope, std.error, and p.value in table output
linear_fits <- linear_fits %>%
  select(Sample, estimate, std.error, p.value) 
 
#Displaying the table and specifying sig digits shown
 linear_fits %>%
  kable(digits=3)

Sample	estimate	std.error	p.value
1001_C	0.167	1.606	0.927
1001_W	1.309	0.461	0.105
1002	4.016	1.478	0.113
1003	-4.607	2.589	0.217
1004	-22.088	2.833	0.016

NA

Can only import one image at a time right now, we could choose some notable ones to display?